Predictive Kinetic Energy Management for Large Electric Vehicles using Radar Information

Predictive or look-ahead strategies that attempt to incorporate upcoming environmental (e.g. road topography, visibility) and traffic state (e.g. preceding vehicles, speed limits, traffic signal state/schedule) information for the purposes of optimizing vehicular energy consumption have been attracting a lot of attention in the past decade. In this work, we propose and evaluate a radar-based predictive kinetic energy management (PKEM) scheme that is applicable as an add-on driver assistance module for a large electric vehicle. This paper details our approach to modeling each subsystem of the framework including the interacting multiple model radar filter, the model predictive controller, and the powertrain. We found that there are clear energy saving benefits for the PKEM scheme with minimal compromises on travel time. These benefits can be in the order of 10-12% over the baseline driver-only case in urban environments and are negligible on the highway cycle. Results included demonstrate the potential trade-offs and accommodations of driver desired inputs.